{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Support Vector Machines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's create the same fake income / age clustered data that we used for our K-Means clustering example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "#Create fake income/age clusters for N people in k clusters\n",
    "def createClusteredData(N, k):\n",
    "    pointsPerCluster = float(N)/k\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range (k):\n",
    "        incomeCentroid = np.random.uniform(20000.0, 200000.0)\n",
    "        ageCentroid = np.random.uniform(20.0, 70.0)\n",
    "        for j in range(int(pointsPerCluster)):\n",
    "            X.append([np.random.normal(incomeCentroid, 10000.0), np.random.normal(ageCentroid, 2.0)])\n",
    "            y.append(i)\n",
    "    X = np.array(X)\n",
    "    y = np.array(y)\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Je1+tVavvst6zd8eEJzl5vW4O2b9fCiISEWkeLpeL4y4+nONqOUL2J785jXvO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\nNmU6VggBHFwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21a179fe6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from pylab import *\n",
    "\n",
    "(X, y) = createClusteredData(100, 5)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(X[:,0], X[:,1], c=y.astype(np.float))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now we'll use linear SVC to partition our graph into clusters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm, datasets\n",
    "\n",
    "C = 1.0\n",
    "svc = svm.SVC(kernel='linear', C=C).fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "By setting up a dense mesh of points in the grid and classifying all of them, we can render the regions of each cluster as distinct colors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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F86GWe002Up5WCCHEUI27YN6hY/15xjc/nnTp9sGQNfBCCCGGatwG8440st7U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\nSeq4WpJ5RNwYEa9ExOGIcLe4EUTE6xHxQkQcmJ0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x21a17a0b6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plotPredictions(clf):\n",
    "    xx, yy = np.meshgrid(np.arange(0, 250000, 10),\n",
    "                     np.arange(10, 70, 0.5))\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)\n",
    "    plt.scatter(X[:,0], X[:,1], c=y.astype(np.float))\n",
    "    plt.show()\n",
    "    \n",
    "plotPredictions(svc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Or just use predict for a given point:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "print(svc.predict([[200000, 40]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "print(svc.predict([[50000, 65]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Activity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "\"Linear\" is one of many kernels scikit-learn supports on SVC. Look up the documentation for scikit-learn online to find out what the other possible kernel options are. Do any of them work well for this data set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
